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Adversary Instantiation - Lower Bounds for Differentially Private Machine Learning

Adversary Instantiation - Lower Bounds for Differentially Private Machine Learning

IEEE Symposium on Security and Privacy via YouTube Direct link

Intro

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Intro

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Adversary Instantiation - Lower Bounds for Differentially Private Machine Learning

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  1. 1 Intro
  2. 2 Machine Learning Is Not Private
  3. 3 Machine learning with Differential Privacy
  4. 4 We want to calculate the epsilon.
  5. 5 We Focus on DPSGD!
  6. 6 Membership inference
  7. 7 Worst-Case Example
  8. 8 Intermediate Model Access
  9. 9 Adaptive Intermediate Model Acce Distinguisher
  10. 10 Gradient Poisoning Attack

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